Evaluation of the Hydrophilic, Cohesive, and Physical Properties of Eight Hyaluronic Acid Fillers: Clinical Implications of Gel Differentiation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Hyaluronic acid (HA) fillers are used to treat an array of aesthetic indications. Proper filler selection is paramount for successful patient outcomes. However, many important physiochemical and physical properties that impact HA gel behavior remain undefined. Purpose: To evaluate the hydrophilicity, cohesivity and particle size of eight commercial HA fillers manufactured by either Non-Animal Stabilized Hyaluronic Acid (NASHA) or Optimal Balance Technology (OBT) techniques. Methods and Materials: Three individual in vitro experiments were performed to assess HA swelling capacity, cohesion, and particle size. Image analyses, blinded evaluation using the Gavard-Sundaram Cohesivity Scale, and laser diffraction technology were utilized, respectively. Results: Compared to fillers manufactured with NASHA technology, OBT products demonstrated greater swelling capacity, cohesion, and wider particle size distributions. Strong positive correlations between swelling factor, degree of cohesivity, and increasing widths of the particle size distributions were observed. Conclusions: The hydrophilicity, cohesivity and particle size distributions vary among HA fillers manufactured with different techniques. The creation of new labels identifying products based on their unique combination of physiochemical and physical characteristics may help guide appropriate selection of HA fillers to optimize patient outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it